r/learnAIAgents

Hey everyone,

I’m trying to figure out what skills I should focus on if my goal is to actually make money with AI and stay ahead of the competition.

I’m not trying to build or train my own AI models from scratch. I’m more interested in using existing AI models and tools to build practical things like:

• AI agents
• automation systems
• business workflows
• internal tools
• frameworks
• AI-powered services for companies

Right now, I’m trying to avoid wasting time learning things that sound impressive but don’t actually help me build valuable systems or create income.

So my questions are:

1. What are the most important skills to learn if I want to build useful AI systems with existing models?
2. Should I learn Python, and if yes, how deep should I go?
3. What tools/software should I focus on? For example n8n, APIs, Supabase, LangChain, RAG, vector databases, etc.
4. What should I avoid learning for now?
5. What would you focus on if you were starting today but wanted to be ahead of the average “AI automation” person?
6. Are there any specific courses, YouTube channels, books, communities, documentation, or projects you would recommend learning from?

My current thinking is that the valuable skills are probably around APIs, automation, databases, structured outputs, RAG, agents, scraping, and business process automation, but I’m not sure what order to learn them in.

I’d also really appreciate recommendations for the best places to learn these things properly. I’m not looking for hype or guru content, but practical resources that help me build real AI systems and understand the technical/business side.

I’d appreciate honest advice from people who are actually building with AI or working in the field.

We are learning to build agents. Our support team gets 200 tickets a day through a typeform. Someone has to read each one, tag priority, route to engineering or CS, and reply with an initial answer.

I want to build an agent that reads the form, classifies intent, checks our docs for an answer, replies if confidence is high, or routes with context if not. We tried a basic Zap but it cannot read the long text fields well. What architecture are people using for form automation like this when you are just starting?

I'm 19, skipped college, and have been building AI products full time for over a year.

Every single day I use AI. And every single day I hit the same wall.

Stop what I'm doing. Open a tab. Explain my context. Ask my question. Copy the answer back. Return to work. Repeat 30 times a day.

That's not AI working for me. That's me working for AI.

The chat box made sense in 2022. It was the easiest interface to ship. But three years later we're still using the same pattern and nobody has questioned whether it's actually right.

It's not.

The real version of AI doesn't sit in a tab waiting for you to remember to ask it something. It already knows what you're working on. It acts before you ask.

OpenClaw proved people want this. 247k GitHub stars for a tool that deleted inboxes and ran up $3,600 API bills. The demand is real. The execution wasn't there.

So I built Clarko with my brother. It sits in the background of your desktop, sees your screen, hears your audio, and acts without being asked. It already has full context on what you're doing so it helps before you even realize you need it.

The part that matters: everything runs locally on your machine. No cloud. No screenshots sent anywhere. No company seeing your screen. We built it this way specifically because Microsoft Recall showed exactly what happens when you don't. Your data stays yours.

It suggests actions and you approve or decline. You're always in control of what it touches. Over time it learns your workflow and gets smarter about what you actually want handled. One hotkey and you can tell it to do anything inside any app instantly.

We just opened the waitlist at clarko.ai

What would you want an AI like this to handle for you?

As a digital marketer, which AI tools do you think are must-learn in today’s time?

We’ve been spending a lot of time stress-testing AI agents lately, and honestly, the biggest surprise wasn’t hallucinations.

It was how easy it was to change an agent’s behavior through completely normal-looking conversations.

A few things we kept running into:

• agents leaking hidden instructions
• support bots making up policies that don’t exist
• tools getting triggered in ways they shouldn’t
• memory/context getting mixed between sessions
• prompts that looked “safe” failing after a few back-and-forth messages
• indirect prompt injection through retrieved content

What really stood out was that most of these systems actually looked fine during normal QA/testing.

The issues only started showing up once we interacted with them more like real users… or people intentionally trying to push boundaries.

Feels like a lot of teams are still mainly testing:
“Does the agent work?”

instead of:
“How does the agent behave when things get weird?”

Curious how others here are approaching testing before deployment.

Are you mostly doing:

• manual testing?
• adversarial prompting?
• eval pipelines?
• simulations?
• red teaming?

I keep seeing this happen. An AI agent runs fine for a while, then just stops. Or it starts making mistakes it wasn't making before. Or it hits an error it can't get past.

And as a non-technical founder, it feels like you have no idea where to start. You can't just open it up and magically figure out what went wrong. You're just stuck.

Has this happened to you? What did you do?

Has anyone here deployed an AI employee (like an autonomous agent that handles sales, research, or outreach) and it just stopped working out of nowhere?

Like it was running fine and then started drifting from the original instructions, or hit an API error it couldn't recover from, or started making basic mistakes it wasn't making before.

did you figure out what went wrong? did you feel like you had any way to fix it without calling in a developer?

I'm genuinely if this is a common issue or if I'm just seeing it in my corner of the internet.

Looking for a discussion and guidance from people implementing AI agent workflow or multi agent systems for enterprises.

If you leverage Text, Image and Video generation in your systems, please DM. I am looking for guidance on deployment.

Hi All,

Trying to get my hosted MCP Server(US rental market) in front of more AI Agent developers.

I’ve done a lot of direct outreach, but I’m trying to figure out where agent developers hang out and talk shop. Any suggestions on certain communities that are geared toward Agent development?

I’m continuing the direct day to day grind, but looking to expand.

Thanks in advance for any advice.

Hey everyone,

I currently have 6 micro-SaaS live, bringing in a bit over $20k in MRR.

The crazy part? I barely wrote a single line of code. I used AI to generate everything, from the database to the UI.

It wasn’t magic on day one. I spent hours stuck on broken code before I finally cracked the system:

• Keeping the idea tiny (a true MVP).
• Prompting the AI step-by-step.
• Launching fast to get real traction.

Lately, I see too many non-tech people give up at the first AI bug. It sucks because the technical barrier is basically gone.

So, I’m starting a Skool community.

Full transparency: I will probably charge for the full course down the line. It makes sense given the exact workflows and copy-paste prompts I’ll be sharing.

But the main goal right now is to build together. Building alone is the fastest way to quit.

If you want to join and build your own AI SaaS with us: drop a comment or shoot me a DM, and I’ll send you the invite!

Lately I’ve been experimenting with a different workflow for AI coding tools.

Instead of relying on temporary chats (that forget context, crash, or hit limits), I’m thinking about building a persistent AI coding environment on a cloud VM.

The idea is something like:

- Ubuntu server

- Ollama

- Qwen / DeepSeek coder models

- OpenClaw or OpenHands

- GitHub repo connection

- PROJECT_CONTEXT.md + TASK_PROGRESS.md memory system

So the AI agent can:

- remember the project structure

- track progress between sessions

- continue after crashes

- work more like a persistent developer workspace

I’m especially interested in:

- whether this is actually practical long term

- how stable these agent workflows are

- whether local open-source models are enough for real SaaS development

- and how people manage context/memory across long projects

Does anyone here actively use a setup like this?

Would love to hear real experiences before I go deeper into building it.

I started messing with ai agents end of last year and kept drowning in hype threads and random buzz around every new thing. I wanted something that worked without spending weeks guessing my way through it.

I build agents for my day job but I’m still super casual about the tools I reach for. None of this is fancy insider stuff. it’s just what made the whole thing feel doable instead of overwhelming.

GPTs were my first steady setup. OpenAI custom assistants make simple personal agents way less painful. you spin one up, tweak it a bit, and it handles most everyday tasks without needing to write a whole system. Could someone code a stronger one? sure, but for most people starting out, this route removes a ton of friction.

N8n became the thing I leaned on once I needed an agent to actually hit tools or run automations. it’s flexible, open source, and you can host it yourself. every time I tried other no code platforms, I kept coming back because n8n felt less boxed in.

Once I wanted multi agent setups, python frameworks started to matter. CrewAI worked well for me. people argue endlessly over which one is “best”, but CrewAI was stable and clear enough that I could actually ship something without wrestling the whole stack.

A little bonus combo that helped me level up: CursorAI paired with CrewAI. Cursor writes the boilerplate, sets up patterns, and gets you moving faster. telling it to scaffold a team of agents through CrewAI saved me hours.

For anything that needed a simple front end, I used Streamlit. super quick to get something on-screen, especially when I needed a little UI for an n8n workflow. if you tell Cursor to build the Streamlit part, it usually nails the structure.

The biggest lesson I wish I knew early on is that an agent is mostly just a tiny bit of logic living online with access to an LLM and tools. Once I stopped treating it like some mystical thing, building them felt way lighter.

One other thing worth mentioning is once agents move beyond APIs and start interacting with real apps things do get a bit messy. for some UI-heavy stuff I ended up testing with Askui, which basically lets automation work off what’s actually on screen instead of perfect selectors. It's not something you need from day 1 tho, but it clicked for me later when agents had to deal with real interfaces.

If you’ve been trying to get into this stuff, hope this helps you get moving. Feel free to drop your own setups and tool combos since everyone seems to find their own groove.